Machines

This paper presents a fault-tolerant control (FTC) strategy for a six-degree-of-freedom (DoF) anthropomorphic manipulator operating under actuator faults and complex friction dynamics. The proposed framework integrates a backstepping control methodology with LuGre friction modeling and a feedforward neural network (FFNN) for friction estimation. Actuator faults are considered in the form of multiplicative efficiency losses and additive disturbances. An adaptive control law is developed to estimate and compensate for both friction and actuator faults in real time. The stability of the closed-loop system is guaranteed through Lyapunov theory. The simulation results validate the effectiveness and robustness of the proposed approach in ensuring precise trajectory tracking despite faults and friction uncertainties.

As a critical component of demolition robots, the rotary joint supports the entire manipulator arm and operates under severe loading conditions, rendering it highly susceptible to fatigue failure. To address this challenge, topology optimization is integrated into the structural design to simultaneously enhance fatigue life and achieve lightweighting. In this work, multiple working conditions of the demolition robot are considered and analyzed to identify the extreme operating condition. By extracting the resultant stress on the rotary joint from the assembled structure under the extreme condition, an equivalent model of the independent rotary joint is established. Given that topology optimization based on the original structure could not yield a usable structure, two topology optimization strategies based on resetting the design space are proposed, including topology optimization based on the partially filled design space and topology optimization within the fully filled design space. By performing topology optimization under different schemes, the optimized rotary joint models are reconstructed through geometric fusion. Numerical results demonstrate that the optimized rotary joints exhibit significant improvements in fatigue performance, with fatigue life doubled compared to the original design. Concurrently, the structural mass is effectively reduced. This proposed method achieves the dual objectives of fatigue life enhancement and lightweight design. Furthermore, the results reveal that resetting the design space when topology optimization fails to obtain a usable structure yields superior topology optimization outcomes, providing a valuable new insight for future structural optimization design processes in similar engineering scenarios.

Human-in-the-loop (HIL) immersive simulators integrate a human operator into the simulation loop, enabling real-time interaction with virtual environments. To expose users to controlled acceleration fields, they employ parallel kinematic machines (PKMs), including reduced-degree-of-freedom (DoF) configurations when compact and cost-effective systems are required. These reduced-DoF platforms frequently exhibit kinematic crosstalk, whereby motion along one axis causes unintended displacements or rotations along others. Among compact PKMs, the four-legged, three-DoF platform is widely used, particularly in driving simulators. However, to the best of the authors’ knowledge, its kinematics have never been systematically analyzed in the literature. It is an over-actuated system with specific constraint conditions characterized by actuators that are not fully grounded. As a result, kinematic crosstalk accelerations are not fully determined by kinematic relationships. They also depend on friction at the constraints; thus, they are also determined by the dynamic behavior of the machine, which is difficult to predict during operation. To address this issue, this paper introduces a simplified modeling approach to estimate kinematic crosstalk whose usability is evaluated experimentally both with mono-harmonic, combined DoF tests and in a real-world engineering application on an actual driving simulator. Results show that kinematic crosstalk on the platform is likely to generate acceleration levels up to 4 $\mathrm{m}/{\mathrm{s}}^2$, exceeding the vestibular perception threshold of 0.17 $\mathrm{m}/{\mathrm{s}}^2$ defined by Reid and Nahon. This result is relevant with respect to enabling a comprehensive assessment of the acceleration field to which the user is actually subjected, which determines the actual quality and immersiveness of the simulation.